Ensemble DQN [1]} uses an ensemble of \(N\)  neural networks to estimate state-action values and uses their average to reduce both overestimation bias and estimation variance. Formally, the target value for Ensemble DQN is calculated using
\(Q_E\left(\cdot \right) &= \cfrac{1}{N}\sum _{i=1}^{N}Q_i\left(\cdot \right)\nonumber \\Y_t^E &= R_t + \max \limits _{a^{\prime }\in \mathcal {A}} Q_{E}\left(s_{t+1}, a^{\prime }\right)\) 
